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Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems, Volume II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 3465

Special Issue Editors


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Guest Editor
Discipline of Engineering and Energy, College of Science, Health, Engineering and Education, Murdoch University, Perth, WA 6150, Australia
Interests: autonomous systems; intelligent control; optimization; AI in renewable systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Science and Engineering, Flinders University, Adelaide 5042, Australia
Interests: electrical machines and energy conversion; power electronics and electrical drives; renewable energy systems and energy storage; electric vehicles; power system analysis distributed generation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern electrical and renewable energy systems are currently experiencing significant changes with the recent advances in artificial intelligence (AI) techniques and the standards of industry 4.0.

The complex technical changes are urging modern electrical and renewable energy systems to exhibit more stable and excellent operating performance in terms of effectiveness, persistence, robustness and reliability, design simplicity, and smartness.

However, electrical and renewable energy systems are continuously facing technical challenges and difficulties under parametric and/or structural uncertainties, undesired external disturbances, faults and trips, fast-varying references, sensor noises, nonlinearities, component failures, and the restricted online computing time of control execution.

In order to further address the above concerns and improve the overall performance of electrical and renewable energy systems, many computational intelligence (CI) technologies, such as fuzzy logic, neural networks, reinforcement learning, and evolutionary algorithms, have been utilized for modeling, control, estimation, and optimization of electrical and renewable energy systems. Meanwhile, the recent advancements in microcontrollers and digital signal processing technologies such as DSP and FPGA have facilitated real-time and in-the-loop implementation of CI-based methods for electrical and renewable energy systems.

The main goal of this Special Issue is to highlight the recent advancements, developments, and challenges in CI-based modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems with indications on practical and industry applications.

Topics of interest for publication include, but are not limited to, the following:

  • Fuzzy logic techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based fault detection and prognostics of electrical motor/drive, renewable energy, and power systems
  • Neural network techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based actuators and sensor/data fusion systems design for electrical motor/drive, renewable energy, and power systems
  • Evolutionary algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-based risk and reliability assessment of electrical motor/drive, renewable energy, and power systems
  • Neuro-fuzzy techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • CI-IoT-based integrated frameworks for control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • Deep learning and reinforcement learning for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
  • Stochastic learning and statistical algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems

Dr. Amirmehdi Yazdani
Dr. Amin Mahmoudi
Dr. GM Shafiullah
Dr. Irfan Ahmad Khan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy logic
  • neural networks
  • evolutionary algorithms
  • deep and reinforcement learning

Related Special Issue

Published Papers (4 papers)

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Research

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20 pages, 1571 KiB  
Article
Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles
by Joseph Omakor, Mohamad Alzayed and Hicham Chaoui
Energies 2024, 17(9), 2163; https://doi.org/10.3390/en17092163 - 30 Apr 2024
Viewed by 555
Abstract
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management [...] Read more.
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. Full article
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21 pages, 4682 KiB  
Article
Enhancing Energy Management Strategies for Extended-Range Electric Vehicles through Deep Q-Learning and Continuous State Representation
by Christian Montaleza, Paul Arévalo, Jimmy Gallegos and Francisco Jurado
Energies 2024, 17(2), 514; https://doi.org/10.3390/en17020514 - 20 Jan 2024
Viewed by 889
Abstract
The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization [...] Read more.
The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the “curse of dimensionality” and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability. Full article
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18 pages, 3631 KiB  
Article
Linear Quadratic Gaussian Control of a 6-DOF Aircraft Landing Gear
by Chimezirim Miracle Nkemdirim, Mohamad Alzayed and Hicham Chaoui
Energies 2023, 16(19), 6902; https://doi.org/10.3390/en16196902 - 30 Sep 2023
Viewed by 749
Abstract
The suspension system of the aircraft, provided by the landing gear, is a crucial part of landing, take-off, and taxiing. It is important that this suspension system not only adequately supports the airframe of the aircraft but also provides a comfortable, seamless ride [...] Read more.
The suspension system of the aircraft, provided by the landing gear, is a crucial part of landing, take-off, and taxiing. It is important that this suspension system not only adequately supports the airframe of the aircraft but also provides a comfortable, seamless ride for the passengers. However, the landing gear is usually riddled with issues, such as landing vibrations that affect passenger comfort and cause damage to the aircraft’s airframe. To reduce these vibrations, this paper proposes the use of a Linear Quadratic Gaussian (LQG) controller to control a 6-DOF aircraft landing gear. The LQG controller is an optimal controller that combines the Linear Quadratic Regulator (LQR) controller with the Kalman filter to compute the system’s control signals and estimate the system’s states. In this paper, the state space model of the 6-DOF landing gear is derived, and the mathematical model of the LQG controller is calculated. The controller’s performance is then tested via MATLAB/Simulink and compared with an equally simple control strategy, the PID controller. The results obtained from the testing process indicate that the LQG controller surpasses the PID controller in reducing landing vibrations, maintaining the aircraft’s airframe, and providing passenger comfort. Full article
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Review

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22 pages, 2417 KiB  
Review
Review of the Integration of Hybrid Electric Turbochargers for Mass-Produced Road Vehicles
by Cosmin Constantin Suciu, Sorin Vlad Igret, Ion Vetres and Ioana Ionel
Energies 2024, 17(6), 1484; https://doi.org/10.3390/en17061484 - 20 Mar 2024
Viewed by 664
Abstract
This study presents the findings of a comprehensive SWOT analysis on the integration of hybrid electric turbochargers (HETs) in mass-produced road vehicles. Through a synthesis of multiple research findings, this study compared the performance of HETs on thermal engines versus traditional turbochargers and [...] Read more.
This study presents the findings of a comprehensive SWOT analysis on the integration of hybrid electric turbochargers (HETs) in mass-produced road vehicles. Through a synthesis of multiple research findings, this study compared the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines. The analysis highlights key strengths, weaknesses, opportunities, and threats associated with the adoption of HET technology in the automotive industry. The results of the SWOT analysis provide valuable insights for both manufacturers and consumers regarding the feasibility and benefits of adopting HET technology in modern vehicles. By elucidating the fundamental mechanics of turbochargers and demonstrating the potential of hybrid electric turbocharging, this study contributes to a deeper understanding of the role of HETs in shaping the future of automotive engineering. In conclusion, this study underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emissions and conserving energy. The novelty of this study is reflected in its comprehensive synthesis of multiple research findings, offering insights into the feasibility and benefits of adopting HET technology in modern vehicles, thereby contributing to a deeper understanding of the role of HETs in shaping the future of automotive engineering and highlighting their continued significance, as evidenced by the systematic SWOT analysis presented. Their ability to optimize fuel efficiency and power output, coupled with the feasibility of downsized engines, positions HETs as an attractive option for sustainable mobility solutions. Further research is warranted to comprehensively understand the environmental and economic implications of widespread HET adoption. Full article
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